Corn (Zea mays) kernel processing companies evaluate the quality of kernels to determine
the price of a batch. Human inspectors in labs inspect a reduced set of kernels to estimate
the proportion of damaged kernels in ...[+]

Corn (Zea mays) kernel processing companies evaluate the quality of kernels to determine
the price of a batch. Human inspectors in labs inspect a reduced set of kernels to estimate
the proportion of damaged kernels in any given lot. The visual differences between good
and damaged kernels may be minor and, therefore, difficult to discern. Our goal is to design
a computer vision system that enables the automatic evaluation of the quality of corn lots.
To decide if an individual kernel can be accepted or rejected, it is necessary to design a
method to detect defects, as well as quantify the defective proportions. A setup to work inline
and an approach to identify damaged kernels that combines algorithm-based computer
vision techniques of novelty detection and principal component analysis (PCA) is
presented. Experiments were carried out in three colour spaces using 450 dent corn kernels
previously classified by experts. Results show that the method is promising (92% success)
but extensions are recommended to further improve results.
ª 2013 IAgrE. Published by Elsevier Ltd. All rights reserved.[-]